Supervised machine learning algorithms for predicting student dropout and academic success: a comparative study

Supervised machine learning algorithms for predicting student dropout and academic success: a comparative study

Abstract Utilizing a dataset sourced from a higher education institution, this study aims to assess the efficacy of diverse machine learning algorithms in predicting student dropout and academic success. Our focus was on algorithms capable of effectively handling imbalanced data. To tackle class imb...

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Bibliographic Details
Main Authors: Alice Villar, Carolina Robledo Velini de Andrade
Format: Article
Language:English
Published: Springer 2024-01-01
Series:Discover Artificial Intelligence
Subjects:
Dropout prediction
Supervised classification algorithms
Unsupervised classification algorithms
Class imbalance
Boosting algorithms
Hyperparameters
Online Access:https://doi.org/10.1007/s44163-023-00079-z

Internet

https://doi.org/10.1007/s44163-023-00079-z

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